# A hybrid statistical and machine learning based forecasting framework for the energy sector

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-hybrid-statistical-and-machine-learning-based-forecasting-framework-for-the/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Stefanos G. Baratsas,Funda Iseri,Efstratios N. Pistikopoulos |
| Citations | 15 |
| DOI | 10.1016/j.compchemeng.2024.108740 |
| Fields | Economics, Econometrics and Finance,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://pmc.ncbi.nlm.nih.gov/articles/PMC12001868/pdf/nihms-2048239.pdf |
| OpenAlex ID | https://openalex.org/W4398231127 |
| PMID | 40247844 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Funda Iseri](https://scholariq.org/researchers/funda-iseri/)

## Paper journal

- [Computers & Chemical Engineering](https://scholariq.org/journals/computers-and-chemical-engineering/)

## Paper primary topic

- [Market Dynamics and Volatility](https://scholariq.org/topics/market-dynamics-and-volatility/)

## Paper topics

- [Market Dynamics and Volatility](https://scholariq.org/topics/market-dynamics-and-volatility/)
- [Energy Load and Power Forecasting](https://scholariq.org/topics/energy-load-and-power-forecasting/)
- [Energy, Environment, Economic Growth](https://scholariq.org/topics/energy-environment-economic-growth/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.